Practical Lab 2 - Data Visualization and Publication

This project showcases the capabilities of Matplotlib, Seaborn, and Plotly in visual storytelling for data analysis.

Matplotlib🔗

The Matplotlib Python library allows you to create visualizations and plots. It provides various tools and functions to create multiple graphs, charts, and plots. With Matplotlib, we can generate line plots, scatter plots, bar charts, histograms, and much more.

Data Science Salaries

This project is all about analyzing and visualizing hypothetical data that represents the average salaries for various data science roles over a span of five years. The data includes job titles such as Data Analyst, Machine Learning Engineer, Data Scientist, AI Researcher, and Senior Data Scientist.

1. Importing libraries

import matplotlib.pyplot as plt
import pandas as pd

2. Importing data from CSV and displaying data in table format.

df = pd.read_csv("fake_ds_salaries.csv")
df

	Year	Data Analyst	Machine Learning Eng.	Data Scientist	AI Researcher	Sr. Data Scientist
0	2020	58000	82000	95000	105000	118000
1	2021	62000	87000	98000	112000	127000
2	2022	68000	91000	102000	117000	135000
3	2023	72000	96000	108000	125000	145000
4	2024	76000	102000	115000	133000	155000

3. Plotting the lines on the graph which illustrate the salaries of different data science jobs over 5 years.

fig, ax = plt.subplots()
ax.plot(df["Year"],df["Data Analyst"], label="Data Analyst")
ax.plot(df["Year"],df["Machine Learning Eng."], label="Machine Learning Eng.")
ax.plot(df["Year"],df["Data Scientist"], label="Data Scientist")
ax.plot(df["Year"],df["AI Researcher"], label="AI Researcher")
ax.plot(df["Year"],df["Sr. Data Scientist"], label="Sr. Data Scientist")
ax.legend()

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Plotly🔗

The Matplotlib Python library allows you to create visualizations and plots. It provides various tools and functions to create multiple graphs, charts, and plots. With Matplotlib, we can generate line plots, scatter plots, bar charts, histograms, and much more.

Canada's GDP Trends Using Plotly

This project aim to analyze the economic trends of Canada over the last decade through the lens of its Gross Domestic Product (GDP).

1. Importing libraries

import pandas as pd
import plotly.express as px
import plotly

plotly.offline.init_notebook_mode()

2. Importing data from JSON and displaying data in table format.

df = pd.read_json('canada_gdp.json')
df

	year	GDP
0	2014	1822.97
1	2015	1550.54
2	2016	1534.08
3	2017	1677.42
4	2018	1713.84
5	2019	1737.69
6	2020	1628.73
7	2021	1713.80
8	2022	1896.67
9	2023	2010.24

3. Generating a line chart

fig = px.line(df, x="year", y="GDP",title="Canada GDP over last 10 years.")
fig.show()

Seaborn🔗

This project utilizes data visualization techniques to investigate and comprehend the interrelationships between economic and social indicators across diverse countries using Seaborn. The dataset encompasses information on GDP, literacy rates, and GDP per capita for a varied set of countries.

1. Importing libraries

import seaborn as sns
sns.set_theme(style="white")

2. Importing data from CSV and displaying data in table format.

df = pd.read_csv("fake_literacy_data.csv")
df.head()

	literacy_rate	country_name	GDP	GDP_per_capita
0	91.0	Finland	44644.2	55349.8
1	99.0	Denmark	57637.5	75099.5
2	99.0	Switzerland	67789.2	84184.6
3	99.0	Iceland	52062.2	68489.3
4	95.0	Netherlands	55646.5	62549.7

3. Generating a scatterd chart

sctplt = sns.relplot(x="GDP", y="country_name", hue="literacy_rate", size="GDP_per_capita",
            sizes=(100, 400), alpha=.7, palette="muted",
            height=8, data=df)
sctplt.set_ylabels("Country Name")
sctplt._legend.set_title("Literacy Rate and GDP per Capita")
plt.show()

Note: All data used to generate graphs are not real. It is only used for illustative purpose.

Libraries used in this project.

Libraries